{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Data Analytics \n",
    "\n",
    "### Bootstrap Confidence Intervals in Python \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootstrap Confidence Intervals in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of bootstrap for modeling workflows. This should help you get started with this important data analytics method to evaluate and integrate uncertainty in any sample statistics or model.  \n",
    "\n",
    "#### Reporting Uncertainty and Significance\n",
    "\n",
    "With confidence intervals and hypothesis testing we have the opportunity to report uncertainty and to report significance in our statistics. These methods are based on standard methods with their associated limitations and assumptions. For additional learning content to support learning about confidence intervals see [Confidence Intervals Lecture](https://youtu.be/oaXCcTWcU04) for more details and the [Confidence Intervals Interactive Demonstration](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_Confidence_Interval.ipynb) to play with and observe confidence intervals in real-time!\n",
    "\n",
    "Also, for more information about statistical bootstrap see the [Bootstrap Lecture](https://youtu.be/oaXCcTWcU04) and [Bootstrap Workflow](https://git.io/fhgUW) for a general, empirical approach to assess uncertainty in statistics and models and [Bootstrap Interactive Demonstration](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_Bootstrap_Simple.ipynb).\n",
    "\n",
    "This is a tutorial / demonstration of **Bootstrap Confidence Intervals in Python** for data analytics and data science workflows.  In Python, the SciPy package, specifically the [SciPy Stats Functions](https://docs.scipy.org/doc/scipy/reference/stats.html) provides excellent statistics tools. \n",
    "\n",
    "#### Bootstrap\n",
    "\n",
    "The uncertainty in an estimated population parameter from a sample, represented as a range, lower and upper bound, based on a specified probability interval known as the **confidence level**.\n",
    "\n",
    "* one source of uncertainty is the paucity of data.\n",
    "* do 200 or even less sample data provide a precise (and accurate estimate) of the mean? standard deviation? skew? 13th percentile / P13? 3rd central moment? experimental variogram? mutual information? Shannon entropy? etc.\n",
    "\n",
    "Would it be useful to know the uncertainty due to limited sampling?\n",
    "* what is the impact of uncertainty in the mean porosity e.g. 20%+/-2%?\n",
    "\n",
    "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n",
    "\n",
    "Assumptions\n",
    "* sufficient, representative sampling, identical, idependent samples\n",
    "\n",
    "Limitations\n",
    "1. assumes the samples are representative \n",
    "2. assumes stationarity\n",
    "3. only accounts for uncertainty due to too few samples, e.g. no uncertainty due to changes away from data\n",
    "4. does not account for boundary of area of interest \n",
    "5. assumes the samples are independent\n",
    "6. does not account for other local information sources\n",
    "\n",
    "The Bootstrap Approach (Efron, 1982)\n",
    "\n",
    "Statistical resampling procedure to calculate uncertainty in a calculated statistic from the data itself.\n",
    "* Does this work?  Prove it to yourself, for uncertainty in the mean solution is standard error: \n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_\\overline{x} = \\frac{\\sigma^2_s}{n}\n",
    "\\end{equation}\n",
    "\n",
    "Extremely powerful - could calculate uncertainty in any statistic!  e.g. P13, skew etc.\n",
    "* Would not be possible access general uncertainty in any statistic without bootstrap.\n",
    "* Advanced forms account for spatial information and sampling strategy (game theory and Journel’s spatial bootstrap (1993).\n",
    "\n",
    "Steps: \n",
    "\n",
    "1. assemble a sample set, must be representative, reasonable to assume independence between samples\n",
    "\n",
    "2. optional: build a cumulative distribution function (CDF)\n",
    "    * may account for declustering weights, tail extrapolation\n",
    "    * could use analogous data to support\n",
    "\n",
    "3. For $\\ell = 1, \\ldots, L$ realizations, do the following:\n",
    "\n",
    "    * For $i = \\alpha, \\ldots, n$ data, do the following:\n",
    "\n",
    "        * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n",
    "\n",
    "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n",
    "\n",
    "7. Compile and summarize the $L$ realizations of the statistic of interest.\n",
    "\n",
    "* calculate and display the histogram or CDF to see the full distribution\n",
    "\n",
    "* report the percentiles representing the lower and upper confidence intervals, e.g. for a confidence level of 95%, the 2.5 and 97.5 percentiles.\n",
    "\n",
    "This is a very powerful method. Let's demonstrate a bunch of measures.\n",
    "\n",
    "* when available, I compare to the distributions from the analytic expressions.\n",
    "\n",
    "Let's demonstrate for a variety of parameters / statistics:\n",
    "\n",
    "* mean / arithmetic average\n",
    "\n",
    "* proportion\n",
    "\n",
    "* interquartile range\n",
    "\n",
    "* coefficient of variation\n",
    "\n",
    "* correlation coefficient\n",
    "\n",
    "#### Objective \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data_biased.csv at https://git.io/fh0CW\n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # plotting\n",
    "import matplotlib                         # plotting\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "plt.rc('axes', axisbelow=True)            # set axes and grids in the background for all plots\n",
    "import seaborn as sns                     # plotting\n",
    "from scipy import stats                   # summary statistics\n",
    "import math                               # trig etc.\n",
    "import scipy.signal as signal             # kernel for moving window calculation\n",
    "import random                             # random sampling\n",
    "from scipy.stats import gaussian_kde      # for PDF calculation\n",
    "from scipy.stats import t                 # Student's t distribution for analytical solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                     # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Random Number Seed\n",
    "\n",
    "For repeatability, set the random number seed.\n",
    "\n",
    "* this ensures that the same random samples are drawn each run and everyone in the class will have the same results.\n",
    "\n",
    "* change this seed number for a new set of random realizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 73073\n",
    "np.random.seed(seed = seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('sample_data_biased.csv') # load our data table\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv') # load the data from Dr. Pyrcz's GitHub repository"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop some samples so that we increase the variations in bootstrap samples for our demonstration below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 58 number of samples\n"
     ]
    }
   ],
   "source": [
    "df = df.sample(frac = 0.2)                 # extract around 50 random samples to reduce the size of the dataset   \n",
    "print('Using ' + str(len(df)) + ' number of samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the DataFrame would be useful and we already learned about these methods in this [Working with Tabular Data Demo](https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>20</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0.104484</td>\n",
       "      <td>3.003225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>400</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.163054</td>\n",
       "      <td>317.883581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>640</td>\n",
       "      <td>529</td>\n",
       "      <td>1</td>\n",
       "      <td>0.120547</td>\n",
       "      <td>10.801778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>490</td>\n",
       "      <td>289</td>\n",
       "      <td>1</td>\n",
       "      <td>0.144525</td>\n",
       "      <td>16.251107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>470</td>\n",
       "      <td>899</td>\n",
       "      <td>1</td>\n",
       "      <td>0.211944</td>\n",
       "      <td>1302.937858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X    Y  Facies  Porosity         Perm\n",
       "150   20   69       0  0.104484     3.003225\n",
       "17   400  600       1  0.163054   317.883581\n",
       "68   640  529       1  0.120547    10.801778\n",
       "234  490  289       1  0.144525    16.251107\n",
       "214  470  899       1  0.211944  1302.937858"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                  # display first 4 samples in the table as a preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 0 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>58.0</td>\n",
       "      <td>418.275862</td>\n",
       "      <td>231.457153</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>272.500000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>58.0</td>\n",
       "      <td>493.224138</td>\n",
       "      <td>287.393614</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>499.500000</td>\n",
       "      <td>676.500000</td>\n",
       "      <td>989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.395452</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.136501</td>\n",
       "      <td>0.040473</td>\n",
       "      <td>0.074349</td>\n",
       "      <td>0.105662</td>\n",
       "      <td>0.126192</td>\n",
       "      <td>0.147067</td>\n",
       "      <td>0.223661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>58.0</td>\n",
       "      <td>270.961923</td>\n",
       "      <td>590.805185</td>\n",
       "      <td>0.093252</td>\n",
       "      <td>4.112400</td>\n",
       "      <td>15.105651</td>\n",
       "      <td>79.314434</td>\n",
       "      <td>2372.383732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean         std        min         25%         50%  \\\n",
       "X          58.0  418.275862  231.457153  10.000000  272.500000  400.000000   \n",
       "Y          58.0  493.224138  287.393614  39.000000  279.000000  499.500000   \n",
       "Facies     58.0    0.810345    0.395452   0.000000    1.000000    1.000000   \n",
       "Porosity   58.0    0.136501    0.040473   0.074349    0.105662    0.126192   \n",
       "Perm       58.0  270.961923  590.805185   0.093252    4.112400   15.105651   \n",
       "\n",
       "                 75%          max  \n",
       "X         560.000000   990.000000  \n",
       "Y         676.500000   989.000000  \n",
       "Facies      1.000000     1.000000  \n",
       "Porosity    0.147067     0.223661  \n",
       "Perm       79.314434  2372.383732  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Tabular Data with Location Maps \n",
    "\n",
    "It is natural to set the x and y coordinate and feature ranges manually. e.g. do you want your color bar to go from 0.05887 to 0.24230 exactly? Also, let's pick a color map for display. I heard that plasma is known to be friendly to the color blind as the color and intensity vary together (hope I got that right, it was an interesting Twitter conversation started by Matt Hall from Agile if I recall correctly). We will assume a study area of 0 to 1,000m in x and y and omit any data outside this area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0               # range of x values\n",
    "ymin = 0.0; ymax = 1000.0               # range of y values\n",
    "pormin = 0.05; pormax = 0.25;           # range of porosity values\n",
    "permmin = 0.01; permmax = 10000\n",
    "nx = 100; ny = 100; csize = 10.0\n",
    "cmap = plt.cm.plasma                    # color map\n",
    "cumul_prob = np.linspace(0.0,1.0,100)   # list of cumulative probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try out location maps, histograms and scatter plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(231)\n",
    "plt.scatter(df['X'],df['Y'],s = 40,c = df['Porosity'],cmap = plt.cm.inferno,linewidths = 0.3,edgecolor = 'black',alpha = 0.8,vmin = pormin,vmax = pormax)\n",
    "plt.colorbar(); plt.xlabel('X (m)'); plt.ylabel('Y (m)'); plt.title('Porosity Location Map')\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.hist(df['Porosity'],color = 'darkorange',alpha = 0.7,edgecolor='black',bins = np.linspace(pormin,pormax,int(len(df)/3)))\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram')\n",
    "plt.grid(True, which='major',linewidth = 1.0); plt.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.scatter(df['X'],df['Y'],s = 40,c = df['Perm'],cmap = plt.cm.inferno,linewidths = 0.3,edgecolor = 'black',alpha = 0.8,vmin = permmin,vmax = permmax,norm=matplotlib.colors.LogNorm())\n",
    "plt.colorbar(); plt.xlabel('X (m)'); plt.ylabel('Y (m)'); plt.title('Permeability Location Map')\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.hist(df['Perm'],color = 'darkorange',alpha = 0.7,edgecolor='black',bins=np.logspace(np.log10(permmin),np.log10(permmax),int(len(df)/3)))\n",
    "#sns.kdeplot(x=df['Perm'],color = 'black',alpha = 0.2,levels = 1,log_scale = True,bw_adjust = 1)\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Permeability Histogram'); plt.xscale('log')\n",
    "plt.grid(True, which='major',linewidth = 1.0); plt.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.scatter(df['Porosity'],df['Perm'],s = 40,color = 'darkorange',alpha = 0.7,edgecolor='black')\n",
    "#plt.contour(df['Porosity'],df['Perm'] Z, colors='black');\n",
    "plt.ylabel('Permeability (mD)'); plt.xlabel('Porosity (fraction)'); plt.title('Permeability-Porosity Scatter Plot')\n",
    "plt.yscale('log')\n",
    "sns.kdeplot(x=df['Porosity'],y=df['Perm'],color = 'black',alpha = 0.2,levels = 4)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=2.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootstrap Method in Python                       \n",
    "\n",
    "If you are new to bootstrap and Python, here's the most simple code possible for bootstrap.\n",
    "\n",
    "* specify the number of bootstrap realizations, $L$\n",
    "* declare a list to store the bootstrap realizations of the statistic of interest\n",
    "* loop over L bootstrap realizations\n",
    "    * n MCS, random samples with replacement for a new realization of the data\n",
    "    * calculate the realization of the statistic from the realization of the data\n",
    "* summarize the resulting uncertainty model, histogram, summary statistics etc.\n",
    "\n",
    "#### Bootstrap of the Sample Mean, Arithmetic Average\n",
    "\n",
    "Let's demonstrate bootstrap for uncertainty in the arithmetic average with a simple workflow.\n",
    "\n",
    "```python\n",
    "por_avg_real = []                      # declare an empty list to store the bootstrap realizations of the statistic \n",
    "for k in range(0,L):                   # loop over the L bootstrap realizations\n",
    "    samples = random.choices(df['Porosity'].values, k=len(df)) # n Monte Carlo simulations\n",
    "    por_avg_real.append(np.average(samples)) # calculate the statistic of interest from the new bootstrap dataset\n",
    "```\n",
    "\n",
    "We will compare the bootstrap uncertainty in the sample arithmetic average distribution with the analytical expression:\n",
    "\n",
    "\\begin{equation}\n",
    "\\overline{x} \\pm t_{\\frac{\\alpha}{2},n-1}\\sqrt{\\frac{s^2}{n}}\n",
    "\\end{equation}\n",
    "\n",
    "The remaining code in this block is just a super cool set of plots with the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bootstrap method for uncertainty in the sample mean\n",
    "L = 1000                               # set the number of bootstrap realizations   \n",
    "por_avg_real = []                      # declare an empty list to store the bootstrap realizations of the statistic \n",
    "for k in range(0,L):                   # loop over the L bootstrap realizations\n",
    "    samples = random.choices(df['Porosity'].values, k=len(df)) # n Monte Carlo simulations\n",
    "    por_avg_real.append(np.average(samples)) # calculate the statistic of interest from the new bootstrap dataset\n",
    "\n",
    "# Analytical solution for the uncertainty in the sample mean, Student's t distribution with the correct mean and standard deviation\n",
    "analytical = t.pdf(np.linspace(pormin,pormax,100), len(df)-1,loc=np.average(df['Porosity']),scale=math.sqrt(np.var(df['Porosity'].values)/len(df)))\n",
    "\n",
    "# Plot of the original data and bootstap and analytical results   \n",
    "fig = plt.subplot(131) \n",
    "plt.hist(df['Porosity'],color = 'darkorange',alpha = 0.8,edgecolor='black',linewidth=2,bins = np.linspace(pormin,pormax,int(len(df)/3)))\n",
    "#plt.plot([np.average(df['Porosity']),np.average(df['Porosity'])],[0,100])\n",
    "plt.axvline(x=np.average(df['Porosity']),linestyle=\"--\",c='black')\n",
    "plt.text(np.average(df['Porosity'])+0.005, 8.8, r'Average = ' + str(round(np.average(df['Porosity']),3)), fontsize=12)\n",
    "plt.text(np.average(df['Porosity'])+0.005, 8.3, r'St.Dev. = ' + str(round(np.std(df['Porosity']),3)), fontsize=12)\n",
    "plt.text(np.average(df['Porosity'])+0.005, 7.8, r'P10 = ' + str(round(np.percentile(df['Porosity'],10),3)), fontsize=12)\n",
    "plt.text(np.average(df['Porosity'])+0.005, 7.3, r'P90 = ' + str(round(np.percentile(df['Porosity'],90),3)), fontsize=12)\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Original Porosity Histogram')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.hist(por_avg_real,color = 'darkorange',alpha = 0.8,edgecolor = 'black',bins=np.linspace(pormin,pormax,100),label = 'bootstrap',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "plt.plot(np.linspace(pormin,pormax,100),analytical,color = 'black',linewidth=3,label = 'analytical',alpha=0.8)\n",
    "plt.axvline(x=np.average(por_avg_real),linestyle=\"--\",c='black')\n",
    "plt.fill_between(np.linspace(pormin,pormax,100), 0, analytical, where = np.linspace(pormin,pormax,100) <= np.percentile(por_avg_real,10), facecolor='red', interpolate=True, alpha = 0.5)\n",
    "plt.fill_between(np.linspace(pormin,pormax,100), 0, analytical, where = np.linspace(pormin,pormax,100) >= np.percentile(por_avg_real,90), facecolor='red', interpolate=True, alpha = 0.5)\n",
    "plt.text(np.average(por_avg_real)+0.009, L*0.07, r'Average = ' + str(round(np.average(por_avg_real),3)), fontsize=12)\n",
    "plt.text(np.average(por_avg_real)+0.009, L*0.066, r'St.Dev. = ' + str(round(np.std(por_avg_real),3)), fontsize=12)\n",
    "plt.text(np.average(por_avg_real)+0.009, L*0.062, r'P90 = ' + str(round(np.percentile(por_avg_real,90),3)), fontsize=12)\n",
    "plt.text(np.average(por_avg_real)+0.009, L*0.058, r'P10 = ' + str(round(np.percentile(por_avg_real,10),3)), fontsize=12)\n",
    "plt.xlabel('Bootstrap Realizations and Analytical Uncertainty Distribution for Mean'); plt.ylabel('Frequency'); plt.title('Mean Bootstrap Realizations')\n",
    "plt.legend(loc = 'upper left')\n",
    "\n",
    "fig = plt.subplot(133) \n",
    "plt.hist(df['Porosity'],color = 'darkorange',alpha = 0.8,edgecolor='black',linewidth=2,bins = np.linspace(pormin,pormax,int(len(df)/3)))\n",
    "#plt.plot([np.average(df['Porosity']),np.average(df['Porosity'])],[0,100])\n",
    "plt.axvline(x=np.average(df['Porosity']),c='black')\n",
    "plt.axvline(x=np.percentile(por_avg_real,90),linestyle=\"--\",c='black')\n",
    "plt.axvline(x=np.percentile(por_avg_real,10),linestyle=\"--\",c='black')\n",
    "plt.text(np.percentile(por_avg_real,90)+0.009,8.8, r'Average = ' + str(round(np.average(por_avg_real),3)), fontsize=12)\n",
    "plt.text(np.percentile(por_avg_real,90)+0.009,8.3, r'Average P90 = ' + str(round(np.percentile(por_avg_real,90),3)), fontsize=12)\n",
    "plt.text(np.percentile(por_avg_real,90)+0.009,7.8, r'Average P10 = ' + str(round(np.percentile(por_avg_real,10),3)), fontsize=12)\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram with Uncertainty in Average')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bootstrap of the Proportion\n",
    "\n",
    "Let's demonstrate another case for which we have the analytical solution. \n",
    "\n",
    "We will compare the bootstrap results with the analytical expression:\n",
    "\n",
    "\\begin{equation}\n",
    "\\hat{p} \\pm t_{\\frac{\\alpha}{2},n-1}\\sqrt{\\frac{\\hat{p}(1-\\hat{p})}{n}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 100000                               # set the number of bootstrap realizations   \n",
    "sand_prop_real = []                      # declare an empty list to store the bootstrap realizations of the statistic \n",
    "shale_prop_real = []\n",
    "for k in range(0,L):                     # loop over the L bootstrap realizations\n",
    "    samples = random.choices(df['Facies'].values, k=len(df)) # n Monte Carlo simulations\n",
    "    sand_prop_real.append(samples.count(1)/len(df)) # calculate the statistic of interest from the new bootstrap dataset\n",
    "    shale_prop_real.append(samples.count(0)/len(df)) # calculate the statistic of interest from the new bootstrap dataset       \n",
    "sand_prop = np.sum(df['Facies'] == 1)/len(df); shale_prop = np.sum(df['Facies'] == 0)/len(df)\n",
    "\n",
    "analytical_shale = t.pdf(np.linspace(0.0,1.0,100), len(df)-1,loc=shale_prop,scale=math.sqrt(shale_prop*(1.0-shale_prop)/len(df)))\n",
    "\n",
    "fig = plt.subplot(121) \n",
    "barlist = plt.bar(x=['Shale','Sand'],height = [1-sand_prop,sand_prop],color = 'gold',alpha = 0.8,edgecolor='black',linewidth=2)\n",
    "barlist[0].set_color('brown'); barlist[0].set_edgecolor('black')\n",
    "plt.text(-.3, shale_prop+0.02, r'Prop. Shale = ' + str(round(shale_prop,2)), fontsize=12)\n",
    "plt.text(0.7, sand_prop+0.02, r'Prop. Sand = ' + str(round(sand_prop,2)), fontsize=12)\n",
    "plt.xlabel('Rock Type / Facies'); plt.ylabel('Proportion'); plt.title('Sample Histogram')\n",
    "plt.ylim([0,1]); plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "plt.subplot(122)     \n",
    "plt.hist(sand_prop_real,color = 'gold',alpha = 0.8,edgecolor = 'black',linewidth=2,bins=np.linspace(0.0,1.0,40),label = 'Sand',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "analytical = t.pdf(np.linspace(0.0,1.0,100), len(df)-1,loc=sand_prop,scale=math.sqrt(sand_prop*(1.0-sand_prop)/len(df)))\n",
    "plt.plot(np.linspace(0.0,1.0,100),analytical,color = 'black',label = 'Analytical',alpha=0.8)\n",
    "plt.axvline(x=sand_prop,linestyle=\"--\",c='black')\n",
    "plt.fill_between(np.linspace(0.0,1.0,100), 0, analytical, where = np.linspace(0.0,1.0,100) <= np.percentile(sand_prop_real,10), facecolor='red', interpolate=True, alpha = 0.8,label='Outside')\n",
    "plt.fill_between(np.linspace(0.0,1.0,100), 0, analytical, where = np.linspace(0.0,1.0,100) >= np.percentile(sand_prop_real,90), facecolor='red', interpolate=True, alpha = 0.8)\n",
    "plt.text(np.average(sand_prop_real)+0.04, 7.0, r'Prop. Exp = ' + str(round(sand_prop,2)), fontsize=8)\n",
    "plt.text(np.average(sand_prop_real)+0.04, 6.4, r'Prop. P90 = ' + str(round(np.percentile(sand_prop_real,90),2)), fontsize=8)\n",
    "plt.text(np.average(sand_prop_real)+0.04, 5.8, r'Prop. P10 = ' + str(round(np.percentile(sand_prop_real,10),2)), fontsize=8)\n",
    "\n",
    "plt.hist(shale_prop_real,color = 'brown',alpha = 0.8,edgecolor = 'black',linewidth=2,bins=np.linspace(0.0,1.0,40),label = 'Shale',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "plt.plot(np.linspace(0.0,1.0,100),analytical_shale,color = 'black',alpha=0.8)\n",
    "plt.axvline(x=shale_prop,linestyle=\"--\",c='black')\n",
    "plt.fill_between(np.linspace(0.0,1.0,100), 0, analytical_shale, where = np.linspace(0.0,1.0,100) <= np.percentile(shale_prop_real,10), facecolor='blue', interpolate=True, alpha = 0.8,label='Outside')\n",
    "plt.fill_between(np.linspace(0.0,1.0,100), 0, analytical_shale, where = np.linspace(0.0,1.0,100) >= np.percentile(shale_prop_real,90), facecolor='blue', interpolate=True, alpha = 0.8)\n",
    "plt.text(np.average(shale_prop_real)+0.04, 7.0, r'Prop. Exp = ' + str(round(shale_prop,2)), fontsize=8)\n",
    "plt.text(np.average(shale_prop_real)+0.04, 6.4, r'Prop. P90 = ' + str(round(np.percentile(shale_prop_real,90),2)), fontsize=8)\n",
    "plt.text(np.average(shale_prop_real)+0.04, 5.8, r'Prop. P10 = ' + str(round(np.percentile(shale_prop_real,10),2)), fontsize=8)\n",
    "\n",
    "plt.xlabel('Rock Type / Facies Proportion (fraction)'); plt.ylabel('Frequency'); plt.title('Proportion Bootstrap Realizations and Analytical Uncertainty Distributions')\n",
    "plt.legend(loc = 'upper center')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bootstrap of the Interquartile Range\n",
    "\n",
    "To prove that we can bootstrap any statistic let's select a more complicated measure of dispersion, the interquartile range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 1000                               # set the number of bootstrap realizations   \n",
    "iqr_real = []                      # declare an empty list to store the bootstrap realizations of the statistic \n",
    "for k in range(0,L):                   # loop over the L bootstrap realizations\n",
    "    samples = random.choices(df['Porosity'].values, k=len(df)) # n Monte Carlo simulations\n",
    "    iqr_real.append(np.percentile(samples,75) - np.percentile(samples,25)) # calculate the statistic of interest from the new bootstrap dataset\n",
    "\n",
    "iqr = np.percentile(df['Porosity'],75) - np.percentile(df['Porosity'],25)\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.hist(iqr_real,color = 'darkorange',alpha = 0.8,edgecolor = 'black',linewidth=2,bins=np.linspace(0.0,0.125,100),label = 'bootstrap',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "plt.axvline(x=iqr,c='black')\n",
    "plt.axvline(x=np.percentile(iqr_real,10),linestyle=\"--\",c='black')\n",
    "plt.axvline(x=np.percentile(iqr_real,90),linestyle=\"--\",c='black')\n",
    "sns.kdeplot(x=iqr_real,color = 'black',alpha = 0.7,linewidth = 2,levels = 1,bw_adjust = 1)\n",
    "plt.text(np.percentile(iqr_real,90)+0.009, L*0.04, r'Average = ' + str(round(np.average(iqr_real),3)), fontsize=12)\n",
    "plt.text(np.percentile(iqr_real,90)+0.009, L*0.036, r'St.Dev. = ' + str(round(np.std(iqr_real),3)), fontsize=12)\n",
    "plt.text(np.percentile(iqr_real,90)+0.009, L*0.032, r'P90 = ' + str(round(np.percentile(iqr_real,90),3)), fontsize=12)\n",
    "plt.text(np.percentile(iqr_real,90)+0.009, L*0.028, r'P10 = ' + str(round(np.percentile(iqr_real,10),3)), fontsize=12)\n",
    "plt.xlabel('Boostrap Realizations of Interquartile Range'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Interquartile Range')\n",
    "plt.gca().grid(True, which='major',axis='both',linewidth = 1.0); plt.gca().grid(True, which='minor',axis='x',linewidth = 0.2) # add y grids\n",
    "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bootstrap of the Coefficient of Variation\n",
    "\n",
    "Here's another statistic that requires multiple measures from the each bootstrap realization of the data.\n",
    "\n",
    "* this reinforces that we bootstrap dataset realizations and then calculate the statistic on this dataset realization\n",
    "\n",
    "For the coefficient of variation we will:\n",
    "    \n",
    "* calculate a bootstrap realization of the dataset with $n$ samples with replacement\n",
    "* calculate the mean and standard deviation from this bootstrapped realization of the dataset\n",
    "* calculate a boostrap realization of the coefficient of variation as the standard deviation divided by the mean\n",
    "\n",
    "Repeat this $L$ times and then evaluate the resulting distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 1000                               # set the number of bootstrap realizations   \n",
    "cv_real = []                      # declare an empty list to store the bootstrap realizations of the statistic \n",
    "\n",
    "for k in range(0,L):                   # loop over the L bootstrap realizations\n",
    "    samples = random.choices(df['Porosity'].values, k=len(df)) # n Monte Carlo simulations\n",
    "    cv_real.append(np.std(samples)/np.average(samples)) # calculate the statistic of interest from the new bootstrap dataset\n",
    "\n",
    "cv = np.std(df['Porosity'])/np.average(df['Porosity'])\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.hist(cv_real,color = 'darkorange',alpha = 0.8,edgecolor = 'black',linewidth=2,bins=np.linspace(0.1,0.4,100),label = 'bootstrap',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "plt.axvline(x=cv,c='black')\n",
    "plt.axvline(x=np.percentile(cv_real,10),linestyle=\"--\",c='black')\n",
    "plt.axvline(x=np.percentile(cv_real,90),linestyle=\"--\",c='black')\n",
    "sns.kdeplot(x=cv_real,color = 'black',alpha = 0.8,linewidth=2,levels = 1,bw_adjust = 1)\n",
    "plt.text(np.percentile(cv_real,90)+0.009, 16, r'Average = ' + str(round(np.average(cv_real),3)), fontsize=12)\n",
    "plt.text(np.percentile(cv_real,90)+0.009, 15, r'St.Dev. = ' + str(round(np.std(cv_real),3)), fontsize=12)\n",
    "plt.text(np.percentile(cv_real,90)+0.009, 14, r'P90 = ' + str(round(np.percentile(cv_real,90),3)), fontsize=12)\n",
    "plt.text(np.percentile(cv_real,90)+0.009, 13, r'P10 = ' + str(round(np.percentile(cv_real,10),3)), fontsize=12)\n",
    "plt.xlabel('Boostrap Realizations of Coefficient of Variation'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Coefficient of Variation')\n",
    "plt.gca().grid(True, which='major',axis='both',linewidth = 1.0); plt.gca().grid(True, which='minor',axis='x',linewidth = 0.2) # add y grids\n",
    "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bootstrap of the Correlation Coefficient\n",
    "\n",
    "Here's a statistic that requires us to work with multiple, paired features at once. \n",
    "* this reinforces that we boostrap for a new realization of the dataset, a set of samples with all their features.\n",
    "\n",
    "For the correlation coefficient we will:\n",
    "    \n",
    "* calculate a bootstrap realization of the dataset with $n$ samples with replacement as a new DataFrame with all features\n",
    "* calculate the the correlation coefficient between 2 paired features\n",
    "\n",
    "Repeat this $L$ times and then evaluate the resulting distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 1000                               # set the number of bootstrap realizations   \n",
    "corr_real = []                      # declare an empty list to store the bootstrap realizations of the statistic \n",
    "\n",
    "for k in range(0,L):                   # loop over the L bootstrap realizations\n",
    "    samples = df.sample(n=len(df),replace=True,random_state = seed + k) # n random samples with replacement as a new DataFrame   \n",
    "    corr_real.append(samples[['Porosity','Perm']].corr()['Porosity'][1]) # calculate the statistic of interest from the new bootstrap dataset\n",
    "\n",
    "corr = df[['Porosity','Perm']].corr()['Porosity'][1]\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.hist(corr_real,color = 'darkorange',alpha = 0.8,edgecolor = 'black',linewidth=2,bins=np.linspace(0.5,1.0,100),label = 'bootstrap',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "plt.axvline(x=corr,c='black')\n",
    "plt.axvline(x=np.percentile(corr_real,10),linestyle=\"--\",c='black')\n",
    "plt.axvline(x=np.percentile(corr_real,90),linestyle=\"--\",c='black')\n",
    "sns.kdeplot(x=corr_real,color = 'black',linewidth=2,alpha = 0.8,levels = 1,bw_adjust = 1)\n",
    "# plt.text(np.percentile(corr_real,90)+0.009, 16, r'Average = ' + str(round(np.average(corr_real),3)), fontsize=12)\n",
    "# plt.text(np.percentile(corr_real,90)+0.009, 15, r'St.Dev. = ' + str(round(np.std(corr_real),3)), fontsize=12)\n",
    "# plt.text(np.percentile(corr_real,90)+0.009, 14, r'P90 = ' + str(round(np.percentile(corr_real,90),3)), fontsize=12)\n",
    "# plt.text(np.percentile(corr_real,90)+0.009, 13, r'P10 = ' + str(round(np.percentile(corr_real,10),3)), fontsize=12)\n",
    "plt.xlabel('Boostrap Realizations of Correlation Coefficient'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty Distribution of Correlation Coefficient')\n",
    "plt.gca().grid(True, which='major',axis='both',linewidth = 1.0); plt.gca().grid(True, which='minor',axis='x',linewidth = 0.2) # add y grids\n",
    "plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of bootstrap. \n",
    "\n",
    "* you can use bootstrap to calculate uncertainty in any statistic!\n",
    "\n",
    "* you are calculating a set of realizations of the statistic, representing uncertainty due to small sample size\n",
    "\n",
    "* note the assumptions of bootstrap, including stationarity and representativity\n",
    "\n",
    "* remember, get a dataset realization by bootstrap and then calculate the realization of the statistic from the dataset realization\n",
    "\n",
    "* if your statistic has multiple inputs (e.g. P25 and P75), calculate each from the same bootstrap realization of the dataset.\n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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